CPNet: A Hybrid Neural Network for Identification of Carcinoma Pathological Slices

Runwei Guan, Yanhua Fei, Xiaohui Zhu, Shanliang Yao, Yong Yue, Jieming Ma

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)
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In the medical field, pathological carcinoma images look much more complicated than other medical images. Identifying carcinoma pathology images is a time-consuming and error-prone task for regular doctors and even for some specialists. Nowadays, deep learning has been widely applied in medicine, which could significantly reduce the time cost and improve accuracy. To save time and improve the accuracy of identifying pathological carcinoma slices, we propose a novel ViT-CNN hybrid neural network called CPNet, specially for the classification of different categories of carcinoma pathological slices. CPNet achieves the state-of-the-art performance in PatchCamelyon and our own dataset. We adopt a transfer learning method to identify degrees of malignancy using a few samples. Furthermore, we design and develop a fast medical decision system, where we deploy the CPNet in it. The system could effectively assist doctors in identifying the cancer pathology images with high accuracy and speed. The code of CPNet is in https://github.com/GuanRunwei/CPNet.
Original languageEnglish
Title of host publication2022 7th International Conference on Image, Vision and Computing (ICIVC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)978-1-6654-6734-6
ISBN (Print)978-1-6654-7890-8
Publication statusPublished - 28 Jul 2022


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